Overview

Dataset statistics

Number of variables13
Number of observations9380
Missing cells15146
Missing cells (%)12.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory407.5 B

Variable types

NUM8
CAT5

Warnings

Tax Article has constant value "9380" Constant
Credit Name has a high cardinality: 57 distinct values High cardinality
Notes has 7193 (76.7%) missing values Missing
Number of Taxpayers has 1553 (16.6%) missing values Missing
Amount of Credit has 1553 (16.6%) missing values Missing
Percent of Credit has 1553 (16.6%) missing values Missing
Median Amount of Credit has 1741 (18.6%) missing values Missing
Mean Amount of Credit has 1553 (16.6%) missing values Missing
Median Amount of Credit is highly skewed (γ1 = 27.66914305) Skewed
Mean Amount of Credit is highly skewed (γ1 = 30.7762013) Skewed
Number of Taxpayers has 3222 (34.3%) zeros Zeros
Amount of Credit has 3222 (34.3%) zeros Zeros
Percent of Credit has 3225 (34.4%) zeros Zeros
Median Amount of Credit has 3222 (34.3%) zeros Zeros
Mean Amount of Credit has 3222 (34.3%) zeros Zeros

Reproduction

Analysis started2020-12-13 00:05:00.368190
Analysis finished2020-12-13 00:05:07.535358
Duration7.17 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Tax Year
Real number (ℝ≥0)

Distinct16
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011.035714
Minimum2001
Maximum2016
Zeros0
Zeros (%)0.0%
Memory size73.4 KiB
2020-12-12T19:05:07.591406image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2002
Q12008
median2012
Q32014
95-th percentile2016
Maximum2016
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.203266868
Coefficient of variation (CV)0.002090100558
Kurtosis-0.3098980137
Mean2011.035714
Median Absolute Deviation (MAD)3
Skewness-0.8642538226
Sum18863515
Variance17.66745236
MonotocityNot monotonic
2020-12-12T19:05:07.659465image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%) 
2014117512.5%
 
2013107511.5%
 
2012107511.5%
 
2015104011.1%
 
2016104011.1%
 
2011100010.7%
 
20063253.5%
 
20053253.5%
 
20073153.4%
 
20103153.4%
 
20093103.3%
 
20083103.3%
 
20042803.0%
 
20032803.0%
 
20022752.9%
 
20012402.6%
 
ValueCountFrequency (%) 
20012402.6%
 
20022752.9%
 
20032803.0%
 
20042803.0%
 
20053253.5%
 
20063253.5%
 
20073153.4%
 
20083103.3%
 
20093103.3%
 
20103153.4%
 
ValueCountFrequency (%) 
2016104011.1%
 
2015104011.1%
 
2014117512.5%
 
2013107511.5%
 
2012107511.5%
 
2011100010.7%
 
20103153.4%
 
20093103.3%
 
20083103.3%
 
20073153.4%
 

Tax Article
Categorical

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
9A
9380 
ValueCountFrequency (%) 
9A9380100.0%
 
2020-12-12T19:05:07.734029image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:05:07.778067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:07.818602image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length2
Median length2
Mean length2
Min length2

Overview of Unicode Properties

Unique unicode characters2
Unique unicode categories2 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
9938050.0%
 
A938050.0%
 

Most occurring categories

ValueCountFrequency (%) 
Decimal Number938050.0%
 
Uppercase Letter938050.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
99380100.0%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
A9380100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Common938050.0%
 
Latin938050.0%
 

Most frequent Common characters

ValueCountFrequency (%) 
99380100.0%
 

Most frequent Latin characters

ValueCountFrequency (%) 
A9380100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII18760100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
9938050.0%
 
A938050.0%
 

Credit Type
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
Credit Used
2115 
Credit Earned
2096 
Credit Claimed
1841 
Credit Carried Forward
1796 
Credit Refunded
1532 
ValueCountFrequency (%) 
Credit Used211522.5%
 
Credit Earned209622.3%
 
Credit Claimed184119.6%
 
Credit Carried Forward179619.1%
 
Credit Refunded153216.3%
 
2020-12-12T19:05:07.886660image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:05:07.935703image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:08.002260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length22
Median length14
Mean length14.79520256
Min length11

Overview of Unicode Properties

Unique unicode characters20
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
d2208815.9%
 
e2029214.6%
 
r1866013.4%
 
C130179.4%
 
i130179.4%
 
111768.1%
 
t93806.8%
 
a75295.4%
 
n36282.6%
 
U21151.5%
 
s21151.5%
 
E20961.5%
 
l18411.3%
 
m18411.3%
 
F17961.3%
 
o17961.3%
 
w17961.3%
 
R15321.1%
 
f15321.1%
 
u15321.1%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10704777.1%
 
Uppercase Letter2055614.8%
 
Space Separator111768.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C1301763.3%
 
U211510.3%
 
E209610.2%
 
F17968.7%
 
R15327.5%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
d2208820.6%
 
e2029219.0%
 
r1866017.4%
 
i1301712.2%
 
t93808.8%
 
a75297.0%
 
n36283.4%
 
s21152.0%
 
l18411.7%
 
m18411.7%
 
o17961.7%
 
w17961.7%
 
f15321.4%
 
u15321.4%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
11176100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin12760391.9%
 
Common111768.1%
 

Most frequent Latin characters

ValueCountFrequency (%) 
d2208817.3%
 
e2029215.9%
 
r1866014.6%
 
C1301710.2%
 
i1301710.2%
 
t93807.4%
 
a75295.9%
 
n36282.8%
 
U21151.7%
 
s21151.7%
 
E20961.6%
 
l18411.4%
 
m18411.4%
 
F17961.4%
 
o17961.4%
 
w17961.4%
 
R15321.2%
 
f15321.2%
 
u15321.2%
 

Most frequent Common characters

ValueCountFrequency (%) 
11176100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII138779100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
d2208815.9%
 
e2029214.6%
 
r1866013.4%
 
C130179.4%
 
i130179.4%
 
111768.1%
 
t93806.8%
 
a75295.4%
 
n36282.6%
 
U21151.5%
 
s21151.5%
 
E20961.5%
 
l18411.3%
 
m18411.3%
 
F17961.3%
 
o17961.3%
 
w17961.3%
 
R15321.1%
 
f15321.1%
 
u15321.1%
 

Credit Name
Categorical

HIGH CARDINALITY

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
Farmers' School Tax Credit
 
370
EZ/QEZE Tax Credits - EZ Wage Tax Credit
 
370
EZ/QEZE Tax Credits - EZ Investment Tax Credit
 
370
Investment Tax Credit
 
370
EZ/QEZE Tax Credits - EZ Capital Tax Credit
 
340
Other values (52)
7560 
ValueCountFrequency (%) 
Farmers' School Tax Credit3703.9%
 
EZ/QEZE Tax Credits - EZ Wage Tax Credit3703.9%
 
EZ/QEZE Tax Credits - EZ Investment Tax Credit3703.9%
 
Investment Tax Credit3703.9%
 
EZ/QEZE Tax Credits - EZ Capital Tax Credit3403.6%
 
Investment Tax Credit for the Financial Services Industry3403.6%
 
Special Additional Mortgage Recording Tax Credit3403.6%
 
Long-Term Care Insurance Credit3153.4%
 
EZ/QEZE Tax Credits - ZEA Wage Credit3103.3%
 
QETC Employment Credit3053.3%
 
EZ/QEZE Tax Credits - QEZE Credit for Real Property Taxes2953.1%
 
EZ/QEZE Tax Credits - QEZE Credit for Real Property Taxes For Corporate Partners2602.8%
 
EZ/QEZE Tax Credits - QEZE Tax Reduction Credit2402.6%
 
EZ/QEZE Tax Credits - QEZE Tax Reduction Credit For Corporate Partners2302.5%
 
Employment of Persons with Disabilities Tax Credit2002.1%
 
QETC Facilities, Operations, and Training Credit1601.7%
 
Conservation Easement Tax Credit1401.5%
 
Clean Heating Fuel Credit1401.5%
 
Fuel Cell Electric Generating Equipment Credit1401.5%
 
Credit for Purchase of an Automated External Defibrillator1401.5%
 
Brownfield Tax Credits - Environmental Remediation Insurance Tax Credit1401.5%
 
Biofuel Production Credit1401.5%
 
QETC Capital Tax Credit1401.5%
 
Brownfield Tax Credits - Remediation Real Property Tax Credit1401.5%
 
Empire State Film Production Credit1401.5%
 
Other values (32)330535.2%
 
2020-12-12T19:05:08.089835image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:05:08.182415image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length89
Median length42
Mean length44.30383795
Min length21

Overview of Unicode Properties

Unique unicode characters63
Unique unicode categories7 ?
Unique unicode scripts2 ?
Unique unicode blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
5288012.7%
 
e383459.2%
 
r312857.5%
 
i299707.2%
 
t295007.1%
 
a232305.6%
 
d174404.2%
 
o163453.9%
 
n163403.9%
 
C149853.6%
 
E144103.5%
 
s126653.0%
 
T109252.6%
 
x96052.3%
 
l93952.3%
 
Z76651.8%
 
m74751.8%
 
c73451.8%
 
u48501.2%
 
p43201.0%
 
g42201.0%
 
Q41851.0%
 
-40601.0%
 
f38900.9%
 
P38750.9%
 
Other values (38)363658.8%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter27782066.9%
 
Uppercase Letter7432017.9%
 
Space Separator5288012.7%
 
Other Punctuation43051.0%
 
Dash Punctuation40601.0%
 
Decimal Number21200.5%
 
Control65< 0.1%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
C1498520.2%
 
E1441019.4%
 
T1092514.7%
 
Z766510.3%
 
Q41855.6%
 
P38755.2%
 
R28503.8%
 
F24303.3%
 
I24053.2%
 
S23803.2%
 
A15502.1%
 
B10651.4%
 
M8601.2%
 
W7851.1%
 
D7601.0%
 
L5950.8%
 
O4800.6%
 
H4600.6%
 
J3700.5%
 
Y3600.5%
 
G2800.4%
 
V2700.4%
 
N2450.3%
 
U1300.2%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
e3834513.8%
 
r3128511.3%
 
i2997010.8%
 
t2950010.6%
 
a232308.4%
 
d174406.3%
 
o163455.9%
 
n163405.9%
 
s126654.6%
 
x96053.5%
 
l93953.4%
 
m74752.7%
 
c73452.6%
 
u48501.7%
 
p43201.6%
 
g42201.5%
 
f38901.4%
 
v33551.2%
 
y24350.9%
 
h22450.8%
 
b18950.7%
 
w13750.5%
 
k1550.1%
 
q1400.1%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
52880100.0%
 

Most frequent Dash Punctuation characters

ValueCountFrequency (%) 
-4060100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
136017.0%
 
628013.2%
 
228013.2%
 
328013.2%
 
028013.2%
 
828013.2%
 
71808.5%
 
51808.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/347580.7%
 
'3708.6%
 
,3207.4%
 
&1403.3%
 

Most frequent Control characters

ValueCountFrequency (%) 
’65100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin35214084.7%
 
Common6343015.3%
 

Most frequent Latin characters

ValueCountFrequency (%) 
e3834510.9%
 
r312858.9%
 
i299708.5%
 
t295008.4%
 
a232306.6%
 
d174405.0%
 
o163454.6%
 
n163404.6%
 
C149854.3%
 
E144104.1%
 
s126653.6%
 
T109253.1%
 
x96052.7%
 
l93952.7%
 
Z76652.2%
 
m74752.1%
 
c73452.1%
 
u48501.4%
 
p43201.2%
 
g42201.2%
 
Q41851.2%
 
f38901.1%
 
P38751.1%
 
v33551.0%
 
R28500.8%
 
Other values (23)236706.7%
 

Most frequent Common characters

ValueCountFrequency (%) 
5288083.4%
 
-40606.4%
 
/34755.5%
 
'3700.6%
 
13600.6%
 
,3200.5%
 
62800.4%
 
22800.4%
 
32800.4%
 
02800.4%
 
82800.4%
 
71800.3%
 
51800.3%
 
&1400.2%
 
’650.1%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII415505> 99.9%
 
None65< 0.1%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
5288012.7%
 
e383459.2%
 
r312857.5%
 
i299707.2%
 
t295007.1%
 
a232305.6%
 
d174404.2%
 
o163453.9%
 
n163403.9%
 
C149853.6%
 
E144103.5%
 
s126653.0%
 
T109252.6%
 
x96052.3%
 
l93952.3%
 
Z76651.8%
 
m74751.8%
 
c73451.8%
 
u48501.2%
 
p43201.0%
 
g42201.0%
 
Q41851.0%
 
-40601.0%
 
f38900.9%
 
P38750.9%
 
Other values (37)363008.7%
 

Most frequent None characters

ValueCountFrequency (%) 
’65100.0%
 

Basis Type
Categorical

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.4 KiB
Capital Base
1980 
Total
1980 
Entire Net Income
1980 
Alternative Minimum Tax
1010 
Fixed Dollar Minimum Tax
1010 
Other values (2)
1420 
ValueCountFrequency (%) 
Capital Base198021.1%
 
Total198021.1%
 
Entire Net Income198021.1%
 
Alternative Minimum Tax101010.8%
 
Fixed Dollar Minimum Tax101010.8%
 
Fixed Dollar Minimum97010.3%
 
Alternative Minimum Income4504.8%
 
2020-12-12T19:05:08.268989image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:05:08.322035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:08.394597image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length26
Median length17
Mean length15.5533049
Min length5

Overview of Unicode Properties

Unique unicode characters27
Unique unicode categories3 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
i142809.8%
 
138309.5%
 
a133809.2%
 
e132709.1%
 
t108407.4%
 
l93806.4%
 
n93106.4%
 
m93106.4%
 
o63904.4%
 
r54203.7%
 
x40002.7%
 
T40002.7%
 
M34402.4%
 
u34402.4%
 
I24301.7%
 
c24301.7%
 
E19801.4%
 
N19801.4%
 
F19801.4%
 
d19801.4%
 
D19801.4%
 
C19801.4%
 
p19801.4%
 
B19801.4%
 
s19801.4%
 
Other values (2)29202.0%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter10885074.6%
 
Uppercase Letter2321015.9%
 
Space Separator138309.5%
 

Most frequent Uppercase Letter characters

ValueCountFrequency (%) 
T400017.2%
 
M344014.8%
 
I243010.5%
 
E19808.5%
 
N19808.5%
 
F19808.5%
 
D19808.5%
 
C19808.5%
 
B19808.5%
 
A14606.3%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
i1428013.1%
 
a1338012.3%
 
e1327012.2%
 
t1084010.0%
 
l93808.6%
 
n93108.6%
 
m93108.6%
 
o63905.9%
 
r54205.0%
 
x40003.7%
 
u34403.2%
 
c24302.2%
 
d19801.8%
 
p19801.8%
 
s19801.8%
 
v14601.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
13830100.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin13206090.5%
 
Common138309.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
i1428010.8%
 
a1338010.1%
 
e1327010.0%
 
t108408.2%
 
l93807.1%
 
n93107.0%
 
m93107.0%
 
o63904.8%
 
r54204.1%
 
x40003.0%
 
T40003.0%
 
M34402.6%
 
u34402.6%
 
I24301.8%
 
c24301.8%
 
E19801.5%
 
N19801.5%
 
F19801.5%
 
d19801.5%
 
D19801.5%
 
C19801.5%
 
p19801.5%
 
B19801.5%
 
s19801.5%
 
A14601.1%
 

Most frequent Common characters

ValueCountFrequency (%) 
13830100.0%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII145890100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
i142809.8%
 
138309.5%
 
a133809.2%
 
e132709.1%
 
t108407.4%
 
l93806.4%
 
n93106.4%
 
m93106.4%
 
o63904.4%
 
r54203.7%
 
x40002.7%
 
T40002.7%
 
M34402.4%
 
u34402.4%
 
I24301.7%
 
c24301.7%
 
E19801.4%
 
N19801.4%
 
F19801.4%
 
d19801.4%
 
D19801.4%
 
C19801.4%
 
p19801.4%
 
B19801.4%
 
s19801.4%
 
Other values (2)29202.0%
 

Notes
Categorical

MISSING

Distinct9
Distinct (%)0.4%
Missing7193
Missing (%)76.7%
Memory size73.4 KiB
d/
1638 
1/
204 
3/
 
105
4/
 
86
1/, d/
 
60
Other values (4)
 
94
ValueCountFrequency (%) 
d/163817.5%
 
1/2042.2%
 
3/1051.1%
 
4/860.9%
 
1/, d/600.6%
 
2/510.5%
 
d/, 4/290.3%
 
d/, 3/100.1%
 
2/, d/4< 0.1%
 
(Missing)719376.7%
 
2020-12-12T19:05:08.469662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-12-12T19:05:08.525210image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:08.599273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length3
Mean length2.810767591
Min length2

Overview of Unicode Properties

Unique unicode characters10
Unique unicode categories4 ?
Unique unicode scripts2 ?
Unique unicode blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Most occurring characters

ValueCountFrequency (%) 
n1438654.6%
 
a719327.3%
 
/22908.7%
 
d17416.6%
 
12641.0%
 
41150.4%
 
31150.4%
 
,1030.4%
 
1030.4%
 
2550.2%
 

Most occurring categories

ValueCountFrequency (%) 
Lowercase Letter2332088.5%
 
Other Punctuation23939.1%
 
Decimal Number5492.1%
 
Space Separator1030.4%
 

Most frequent Lowercase Letter characters

ValueCountFrequency (%) 
n1438661.7%
 
a719330.8%
 
d17417.5%
 

Most frequent Other Punctuation characters

ValueCountFrequency (%) 
/229095.7%
 
,1034.3%
 

Most frequent Space Separator characters

ValueCountFrequency (%) 
103100.0%
 

Most frequent Decimal Number characters

ValueCountFrequency (%) 
126448.1%
 
411520.9%
 
311520.9%
 
25510.0%
 

Most occurring scripts

ValueCountFrequency (%) 
Latin2332088.5%
 
Common304511.5%
 

Most frequent Latin characters

ValueCountFrequency (%) 
n1438661.7%
 
a719330.8%
 
d17417.5%
 

Most frequent Common characters

ValueCountFrequency (%) 
/229075.2%
 
12648.7%
 
41153.8%
 
31153.8%
 
,1033.4%
 
1033.4%
 
2551.8%
 

Most occurring blocks

ValueCountFrequency (%) 
ASCII26365100.0%
 

Most frequent ASCII characters

ValueCountFrequency (%) 
n1438654.6%
 
a719327.3%
 
/22908.7%
 
d17416.6%
 
12641.0%
 
41150.4%
 
31150.4%
 
,1030.4%
 
1030.4%
 
2550.2%
 

Number of Taxpayers
Real number (ℝ≥0)

MISSING
ZEROS

Distinct604
Distinct (%)7.7%
Missing1553
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean73.38124441
Minimum0
Maximum5991
Zeros3222
Zeros (%)34.3%
Memory size73.4 KiB
2020-12-12T19:05:08.677341image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q332
95-th percentile318.7
Maximum5991
Range5991
Interquartile range (IQR)32

Descriptive statistics

Standard deviation294.5836481
Coefficient of variation (CV)4.01442699
Kurtosis146.5852792
Mean73.38124441
Median Absolute Deviation (MAD)6
Skewness10.20713178
Sum574355
Variance86779.52573
MonotocityNot monotonic
2020-12-12T19:05:08.759912image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0322234.3%
 
32813.0%
 
41932.1%
 
51882.0%
 
71852.0%
 
61621.7%
 
91421.5%
 
81391.5%
 
101211.3%
 
111061.1%
 
121061.1%
 
131051.1%
 
16810.9%
 
20730.8%
 
18690.7%
 
14680.7%
 
17620.7%
 
15590.6%
 
19530.6%
 
28510.5%
 
22480.5%
 
21430.5%
 
27420.4%
 
29400.4%
 
31390.4%
 
Other values (579)214922.9%
 
(Missing)155316.6%
 
ValueCountFrequency (%) 
0322234.3%
 
32813.0%
 
41932.1%
 
51882.0%
 
61621.7%
 
71852.0%
 
81391.5%
 
91421.5%
 
101211.3%
 
111061.1%
 
ValueCountFrequency (%) 
59911< 0.1%
 
58491< 0.1%
 
57831< 0.1%
 
56541< 0.1%
 
55421< 0.1%
 
53051< 0.1%
 
51611< 0.1%
 
49341< 0.1%
 
32211< 0.1%
 
30911< 0.1%
 

Amount of Credit
Real number (ℝ≥0)

MISSING
ZEROS

Distinct4140
Distinct (%)52.9%
Missing1553
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean28533942.18
Minimum0
Maximum2261711738
Zeros3222
Zeros (%)34.3%
Memory size73.4 KiB
2020-12-12T19:05:08.850990image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median48726
Q33216924.5
95-th percentile93579494.7
Maximum2261711738
Range2261711738
Interquartile range (IQR)3216924.5

Descriptive statistics

Standard deviation147199992.3
Coefficient of variation (CV)5.158768156
Kurtosis83.1283964
Mean28533942.18
Median Absolute Deviation (MAD)48726
Skewness8.428285329
Sum2.233351654e+11
Variance2.166783772e+16
MonotocityNot monotonic
2020-12-12T19:05:08.934562image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0322234.3%
 
80004< 0.1%
 
5161834< 0.1%
 
4646314< 0.1%
 
160004< 0.1%
 
1905914< 0.1%
 
140814< 0.1%
 
18477644< 0.1%
 
930003< 0.1%
 
112743< 0.1%
 
264783< 0.1%
 
321054793< 0.1%
 
54077293< 0.1%
 
3216093< 0.1%
 
39098783< 0.1%
 
10427973< 0.1%
 
37548853< 0.1%
 
575003< 0.1%
 
89273< 0.1%
 
70003< 0.1%
 
20003< 0.1%
 
63993< 0.1%
 
7634123< 0.1%
 
32471793< 0.1%
 
63203< 0.1%
 
Other values (4115)452648.3%
 
(Missing)155316.6%
 
ValueCountFrequency (%) 
0322234.3%
 
821< 0.1%
 
1971< 0.1%
 
2181< 0.1%
 
2872< 0.1%
 
3781< 0.1%
 
6032< 0.1%
 
9541< 0.1%
 
10241< 0.1%
 
10991< 0.1%
 
ValueCountFrequency (%) 
22617117381< 0.1%
 
22163014251< 0.1%
 
21848140051< 0.1%
 
21211552571< 0.1%
 
20123360231< 0.1%
 
19808684881< 0.1%
 
19245874341< 0.1%
 
18283535421< 0.1%
 
17848255071< 0.1%
 
16864675201< 0.1%
 

Percent of Credit
Real number (ℝ≥0)

MISSING
ZEROS

Distinct2409
Distinct (%)30.8%
Missing1553
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean31.50383289
Minimum0
Maximum100
Zeros3225
Zeros (%)34.4%
Memory size73.4 KiB
2020-12-12T19:05:09.025640image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.29
Q366.785
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)66.785

Descriptive statistics

Standard deviation40.03537842
Coefficient of variation (CV)1.270809763
Kurtosis-1.008851353
Mean31.50383289
Median Absolute Deviation (MAD)6.29
Skewness0.843625508
Sum246580.5
Variance1602.831525
MonotocityNot monotonic
2020-12-12T19:05:09.111715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0322534.4%
 
100143215.3%
 
0.1960.1%
 
5.9460.1%
 
4.6260.1%
 
1.760.1%
 
7.4260.1%
 
0.1250.1%
 
7.3950.1%
 
0.1450.1%
 
0.7250.1%
 
0.2750.1%
 
16.6250.1%
 
5.64< 0.1%
 
3.134< 0.1%
 
25.474< 0.1%
 
33.344< 0.1%
 
33.24< 0.1%
 
4.574< 0.1%
 
99.844< 0.1%
 
1.14< 0.1%
 
0.084< 0.1%
 
0.474< 0.1%
 
0.064< 0.1%
 
10.794< 0.1%
 
Other values (2384)306232.6%
 
(Missing)155316.6%
 
ValueCountFrequency (%) 
0322534.4%
 
0.013< 0.1%
 
0.023< 0.1%
 
0.033< 0.1%
 
0.042< 0.1%
 
0.053< 0.1%
 
0.064< 0.1%
 
0.073< 0.1%
 
0.084< 0.1%
 
0.091< 0.1%
 
ValueCountFrequency (%) 
100143215.3%
 
99.981< 0.1%
 
99.871< 0.1%
 
99.844< 0.1%
 
99.811< 0.1%
 
99.681< 0.1%
 
99.652< 0.1%
 
99.631< 0.1%
 
99.521< 0.1%
 
99.472< 0.1%
 

Median Amount of Credit
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct3264
Distinct (%)42.7%
Missing1741
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean61266.91465
Minimum0
Maximum21527410
Zeros3222
Zeros (%)34.3%
Memory size73.4 KiB
2020-12-12T19:05:09.201292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1202
Q314775
95-th percentile167795
Maximum21527410
Range21527410
Interquartile range (IQR)14775

Descriptive statistics

Standard deviation543659.5772
Coefficient of variation (CV)8.873624211
Kurtosis907.280516
Mean61266.91465
Median Absolute Deviation (MAD)1202
Skewness27.66914305
Sum468017961
Variance2.955657359e+11
MonotocityNot monotonic
2020-12-12T19:05:09.285864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0322234.3%
 
2100660.7%
 
500530.6%
 
1250350.4%
 
1000310.3%
 
2500190.2%
 
10000150.2%
 
750130.1%
 
5000110.1%
 
600090.1%
 
700080.1%
 
1100070.1%
 
2560.1%
 
1500060.1%
 
177760.1%
 
135160.1%
 
300060.1%
 
187560.1%
 
194060.1%
 
800060.1%
 
213360.1%
 
5019850.1%
 
1650050.1%
 
16287050.1%
 
219050.1%
 
Other values (3239)407643.5%
 
(Missing)174118.6%
 
ValueCountFrequency (%) 
0322234.3%
 
201< 0.1%
 
211< 0.1%
 
221< 0.1%
 
2560.1%
 
381< 0.1%
 
441< 0.1%
 
471< 0.1%
 
531< 0.1%
 
604< 0.1%
 
ValueCountFrequency (%) 
215274101< 0.1%
 
188471731< 0.1%
 
184084761< 0.1%
 
164159071< 0.1%
 
151690331< 0.1%
 
130451621< 0.1%
 
55730451< 0.1%
 
49500001< 0.1%
 
49497282< 0.1%
 
41401961< 0.1%
 

Mean Amount of Credit
Real number (ℝ≥0)

MISSING
SKEWED
ZEROS

Distinct4055
Distinct (%)51.8%
Missing1553
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean354169.2655
Minimum0
Maximum109656694
Zeros3222
Zeros (%)34.3%
Memory size73.4 KiB
2020-12-12T19:05:09.376442image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3610
Q3101059.5
95-th percentile1381621.3
Maximum109656694
Range109656694
Interquartile range (IQR)101059.5

Descriptive statistics

Standard deviation2276549.145
Coefficient of variation (CV)6.427856301
Kurtosis1376.494345
Mean354169.2655
Median Absolute Deviation (MAD)3610
Skewness30.7762013
Sum2772082841
Variance5.182676009e+12
MonotocityNot monotonic
2020-12-12T19:05:09.462016image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0322234.3%
 
80050.1%
 
615950.1%
 
23334< 0.1%
 
2639664< 0.1%
 
1548774< 0.1%
 
29764< 0.1%
 
146614< 0.1%
 
22554< 0.1%
 
21334< 0.1%
 
1720614< 0.1%
 
21004< 0.1%
 
28503< 0.1%
 
24696523< 0.1%
 
1773593< 0.1%
 
678463< 0.1%
 
14293< 0.1%
 
9774703< 0.1%
 
96433< 0.1%
 
9983< 0.1%
 
18993< 0.1%
 
54733< 0.1%
 
37593< 0.1%
 
90823< 0.1%
 
23113< 0.1%
 
Other values (4030)452048.2%
 
(Missing)155316.6%
 
ValueCountFrequency (%) 
0322234.3%
 
211< 0.1%
 
412< 0.1%
 
441< 0.1%
 
661< 0.1%
 
761< 0.1%
 
1441< 0.1%
 
1911< 0.1%
 
2012< 0.1%
 
2201< 0.1%
 
ValueCountFrequency (%) 
1096566942< 0.1%
 
304743051< 0.1%
 
277714841< 0.1%
 
250017151< 0.1%
 
238745231< 0.1%
 
226579011< 0.1%
 
224553931< 0.1%
 
197005871< 0.1%
 
189836041< 0.1%
 
186560541< 0.1%
 

Group Sort Order
Real number (ℝ≥0)

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212.9211087
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Memory size73.4 KiB
2020-12-12T19:05:09.537581image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile999
Maximum999
Range998
Interquartile range (IQR)3

Descriptive statistics

Standard deviation406.6380856
Coefficient of variation (CV)1.90980635
Kurtosis0.005542608567
Mean212.9211087
Median Absolute Deviation (MAD)2
Skewness1.416151608
Sum1997200
Variance165354.5326
MonotocityNot monotonic
2020-12-12T19:05:09.596632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
999198021.1%
 
3198021.1%
 
2198021.1%
 
1198021.1%
 
5146015.6%
 
ValueCountFrequency (%) 
1198021.1%
 
2198021.1%
 
3198021.1%
 
5146015.6%
 
999198021.1%
 
ValueCountFrequency (%) 
999198021.1%
 
5146015.6%
 
3198021.1%
 
2198021.1%
 
1198021.1%
 

Credit Type Sort Order
Real number (ℝ≥0)

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.232729211
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size73.4 KiB
2020-12-12T19:05:09.656183image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median6
Q37
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.474627665
Coefficient of variation (CV)0.4729133814
Kurtosis-0.742648609
Mean5.232729211
Median Absolute Deviation (MAD)1
Skewness-0.7846569656
Sum49083
Variance6.12378208
MonotocityNot monotonic
2020-12-12T19:05:09.712732image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
6211522.5%
 
1209622.3%
 
5184119.6%
 
8179619.1%
 
7153216.3%
 
ValueCountFrequency (%) 
1209622.3%
 
5184119.6%
 
6211522.5%
 
7153216.3%
 
8179619.1%
 
ValueCountFrequency (%) 
8179619.1%
 
7153216.3%
 
6211522.5%
 
5184119.6%
 
1209622.3%
 

Interactions

2020-12-12T19:05:01.636782image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:01.724357image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:01.808930image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:01.891501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:01.975073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.059646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.146220image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.232294image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.319870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.406945image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.492018image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.573088image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.653657image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.733226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.816797image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.896866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:02.980438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.060507image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.140076image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.215641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.291206image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.369273image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.448841image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.527909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.608979image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.689548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.769117image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.845683image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:03.923750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.002318image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.081386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.159453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.240022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.321092image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.402162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.481730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.560798image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.638865image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.718434image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.796501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.877070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:04.961643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.043714image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.121781image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.200349image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.279417image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.360487image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.440555image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.524127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.605697image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.686267image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.762833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.840900image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:05.918967image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:06.000037image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:06.079105image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:06.159674image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:06.245748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:06.329821image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:06.412392image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:06.497965image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:06.581537image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:06.667111image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:06.750182image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2020-12-12T19:05:09.778789image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-12-12T19:05:09.903396image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-12-12T19:05:10.030505image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-12-12T19:05:10.163620image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-12-12T19:05:10.295233image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-12-12T19:05:06.944850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:07.124004image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:07.269629image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2020-12-12T19:05:07.368715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Sample

First rows

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420159ACredit ClaimedAlternative Fuels and Electric Vehicle Recharging Property CreditEntire Net Incomed/NaNNaNNaNNaNNaN15
520159ACredit ClaimedAlternative Fuels and Electric Vehicle Recharging Property CreditFixed Dollar MinimumNaN3.043478.079.2410000.014493.025
620159ACredit ClaimedAlternative Fuels and Electric Vehicle Recharging Property CreditCapital Based/NaNNaNNaNNaNNaN35
720159ACredit ClaimedAlternative Fuels and Electric Vehicle Recharging Property CreditTotalNaN5.054866.0100.0010000.010973.09995
820159ACredit UsedAlternative Fuels and Electric Vehicle Recharging Property CreditEntire Net Incomed/NaNNaNNaNNaNNaN16
920159ACredit UsedAlternative Fuels and Electric Vehicle Recharging Property CreditFixed Dollar MinimumNaN0.00.00.000.00.026

Last rows

Tax YearTax ArticleCredit TypeCredit NameBasis TypeNotesNumber of TaxpayersAmount of CreditPercent of CreditMedian Amount of CreditMean Amount of CreditGroup Sort OrderCredit Type Sort Order
937020169ACredit UsedWorkers with Disabilities Tax CreditCapital BaseNaN0.00.00.00.00.036
937120169ACredit UsedWorkers with Disabilities Tax CreditTotald/NaNNaNNaNNaNNaN9996
937220169ACredit RefundedWorkers with Disabilities Tax CreditEntire Net IncomeNaN0.00.00.00.00.017
937320169ACredit RefundedWorkers with Disabilities Tax CreditFixed Dollar MinimumNaN0.00.00.00.00.027
937420169ACredit RefundedWorkers with Disabilities Tax CreditCapital BaseNaN0.00.00.00.00.037
937520169ACredit RefundedWorkers with Disabilities Tax CreditTotalNaN0.00.00.00.00.09997
937620169ACredit Carried ForwardWorkers with Disabilities Tax CreditEntire Net IncomeNaN0.00.00.00.00.018
937720169ACredit Carried ForwardWorkers with Disabilities Tax CreditFixed Dollar Minimumd/NaNNaNNaNNaNNaN28
937820169ACredit Carried ForwardWorkers with Disabilities Tax CreditCapital BaseNaN0.00.00.00.00.038
937920169ACredit Carried ForwardWorkers with Disabilities Tax CreditTotald/NaNNaNNaNNaNNaN9998